A Hybrid Framework Combining Model-Based and Data-Driven Methods for Hierarchical Decentralized Robust Dynamic State Estimation

Venkat Krishnan, Yingchen Zhang, Marcos Netto, Lamine Mili, Yoshihiko Susuki

Research output: Contribution to conferencePaper

5 Scopus Citations

Abstract

This paper combines model-based and data-driven methods to develop a hierarchical, decentralized, robust dynamic state estimator (DSE). A two-level hierarchy is proposed, where the lower level consists of robust, model-based, decentralized DSEs. The state estimates sent from the lower level are received at the upper level, where they are filtered by a robust data-driven DSE after a principled sparse selection. This selection allows us to shrink the dimension of the problem at the upper level and hence significantly speed up the computational time. The proposed hybrid framework does not depend on the centralized infrastructure of the control centers; thus it can be completely embedded into the wide-area measurement systems. This feature will ultimately facilitate the placement of hierarchical decentralized control schemes at the phasor data concentrator locations. Also, the network model is not necessary; thus, a topology processor is not required. Finally, there is no assumption on the dynamics of the electric loads. The proposed framework is tested on the 2,000-bus synthetic Texas system, and shown to be capable of reconstructing the dynamic states of the generators with high accuracy, and of forecasting in the advent of missing data.
Original languageAmerican English
Number of pages5
DOIs
StatePublished - 2020
Event2019 IEEE Power & Energy Society General Meeting (PESGM) - Atlanta, Georgia
Duration: 4 Aug 20198 Aug 2019

Conference

Conference2019 IEEE Power & Energy Society General Meeting (PESGM)
CityAtlanta, Georgia
Period4/08/198/08/19

Bibliographical note

See NREL/CP-5D00-72685 for preprint

NREL Publication Number

  • NREL/CP-5D00-76223

Keywords

  • compressed sensing
  • data-driven dynamical systems
  • dynamic state estimation
  • Kalman filtering
  • Koopman mode decomposition
  • sparse selection

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